Redefining Agile for Data Science: A Team First Approach
summary
Argues that Agile works for data science if retooled around team practices rather than individual silos, showing a practical framework used across several teams that shortens feedback loops and raises delivery quality — moving from multi‑month one‑person efforts toward incremental releases where a working model can exist by day 10 not day 120. Uses pairing people, short sprints and sprint demos, and regular retros and planning to force knowledge sharing, reduce single‑person bottlenecks and improve problem‑solving; implementation is hard but produces measurable wins.
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chapters (8)
- 0:00Introduction and welcome
- 1:30Agile for team collaboration
- 4:01Agile fundamentals explained
- 6:50Daily standups in data science
- 8:41Pairing for better results
- 10:48Implementing team-first Agile
- 13:20Why Agile fits data science
- 14:48Challenges and success tips
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